- Artificial Intelligence (AI) is transforming biological research, shifting traditional trial-and-error scientific methods into data-driven precision discovery. - AI models help predict protein structures (like AlphaFold), accelerate drug discovery, and model complex cellular pathways. - Integrates multi-omics data (genomics, proteomics, metabolomics) to decode intricate biological networks and disease mechanisms. - Ethical and operational challenges include data bias, model interpretability ('black box' problem), data privacy, and the need for interdisciplinary training for researchers. **Introduction** The article explores how Artificial Intelligence is opening new frontiers in biological research by revolutionizing fields like structural biology, drug discovery, and genomics. It details how AI algorithms can process vast amounts of multi-omics data to predict protein structures, model cellular interactions, and fast-track medical solutions, while also addressing associated challenges such as data privacy and the black-box nature of AI models. **Core Issue and Detailed Points** - Artificial Intelligence (AI) is transforming biological research, shifting traditional trial-and-error scientific methods into data-driven precision discovery. - AI models help predict protein structures (like AlphaFold), accelerate drug discovery, and model complex cellular pathways. - Integrates multi-omics data (genomics, proteomics, metabolomics) to decode intricate biological networks and disease mechanisms. - Ethical and operational challenges include data bias, model interpretability ('black box' problem), data privacy, and the need for interdisciplinary training for researchers.